Diagnosis of moderate-to-severe hepatic steatosis using deep learning-based automated attenuation measurements on contrast-enhanced CT

IF 2.2 3区医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Abdominal Radiology Pub Date : 2025-03-17 DOI:10.1007/s00261-025-04872-5

Hae Young Kim, Kyung Jin Lee, Seung Soo Lee, Se Jin Choi, Dong Hwan Kim, Subin Heo, Hyeon Ji Jang, Sang Hyun Choi

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Abstract

Purpose

To evaluate the utility of deep learning-based automated attenuation measurements on contrast-enhanced CT (CECT) for diagnosing moderate-to-severe hepatic steatosis (HS), using histology as reference standard.

Methods

This retrospective study included 3,620 liver donors (2,393 men and 1,227 women; mean age, 31.7 ± 9.4 years), divided into the development (n = 2,714) and test (n = 906) cohorts. Attenuation values of the liver and spleen on CECT were measured both manually and using a deep learning algorithm (before and after radiologists’ correction of segmentation errors). Performance of: (1) liver attenuation and (2) liver–spleen attenuation difference for diagnosing moderate-to-severe HS (> 33%) was assessed using the area under the receiver operating characteristic curve (AUC). Three different criteria targeting 95% sensitivity, 95% specificity, and the maximum Youden’s index, respectively, for diagnosing moderate-to-severe HS, were developed and validated.

Results

The performance of deep learning-based measurements did not differ significantly, with or without radiologists’ corrections (p = 0.13). Liver–spleen attenuation difference outperformed liver attenuation alone in diagnosing moderate-to-severe HS in both deep learning-based (AUC, 0.868 vs. 0.821; p = 0.001) and manual (AUC, 0.871 vs. 0.823; p = 0.001) measurements. In the test cohort, the criterion targeting 95% sensitivity for diagnosing moderate-to-severe HS (liver–spleen attenuation difference ≤ 2.8 HU) yielded 92.0% (69/75) sensitivity and 48.5% (403/831) specificity. The criterion targeting 95% specificity (liver–spleen attenuation difference ≤ -18.8 HU) yielded 53.3% (40/75) sensitivity and 95.7% (795/831) specificity. The criterion targeting the maximum Youden’s index (liver–spleen attenuation difference ≤ -8.2 HU) yielded 82.7% (62/75) sensitivity and 80.7% (671/831) specificity.

Conclusion

Deep learning-based automated measurements of liver and spleen attenuation on CECT can be used reliably to detect moderate-to-severe HS.

Graphical Abstract

Abstract Image

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利用基于深度学习的自动衰减测量在增强CT上诊断中重度肝脏脂肪变性。

目的：以组织学为参考标准，评估基于深度学习的对比增强CT （CECT）自动衰减测量在诊断中重度肝脂肪变性（HS）中的应用价值。方法：这项回顾性研究包括3620名肝脏供者(2393名男性和1227名女性；平均年龄（31.7±9.4岁），分为发展组（n = 2714）和试验组（n = 906）。肝脏和脾脏在CECT上的衰减值分别采用人工和深度学习算法测量（放射科医生校正分割误差前后）。采用受者工作特征曲线下面积（AUC）评估(1)肝脏衰减和(2)肝脾衰减差诊断中重度HS（> 33%）的表现。制定并验证了三种不同的标准，分别针对95%的敏感性、95%的特异性和最大约登指数，用于诊断中重度HS。结果：无论是否有放射科医生的校正，基于深度学习的测量效果没有显著差异（p = 0.13）。基于深度学习的肝脾衰减差诊断中重度HS优于单纯肝衰减(AUC, 0.868 vs. 0.821；p = 0.001)和手工(AUC, 0.871 vs. 0.823；P = 0.001)。在试验队列中，诊断中重度HS（肝脾衰减差≤2.8 HU）的目标灵敏度为95%，敏感性为92.0%(69/75)，特异性为48.5%（404 /831）。该标准特异性为95%（肝脾衰减差≤-18.8 HU），敏感性为53.3%(40/75)，特异性为95.7%（795/831）。以最大约登指数（肝脾衰减差≤-8.2 HU）为标准的敏感性为82.7%(62/75)，特异性为80.7%（671/831）。结论：基于深度学习的CECT肝脾衰减自动测量可可靠地用于中重度HS的检测。

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来源期刊

Abdominal Radiology Medicine-Radiology, Nuclear Medicine and Imaging

CiteScore

5.20

自引率

8.30%

发文量

334

期刊介绍： Abdominal Radiology seeks to meet the professional needs of the abdominal radiologist by publishing clinically pertinent original, review and practice related articles on the gastrointestinal and genitourinary tracts and abdominal interventional and radiologic procedures. Case reports are generally not accepted unless they are the first report of a new disease or condition, or part of a special solicited section. Reasons to Publish Your Article in Abdominal Radiology: · Official journal of the Society of Abdominal Radiology (SAR) · Published in Cooperation with: European Society of Gastrointestinal and Abdominal Radiology (ESGAR) European Society of Urogenital Radiology (ESUR) Asian Society of Abdominal Radiology (ASAR) · Efficient handling and Expeditious review · Author feedback is provided in a mentoring style · Global readership · Readers can earn CME credits